01
Find the honest claim
What can this AI feature truthfully promise? That sentence is designed first, because everything else must enforce it.
LLMs · Guardrails · Evaluation
AI Products That Refuse to Fake It
The hard part of an AI product is not calling a model. It is refusing the model's mistakes before your users see them. Our AI products carry that refusal in code: the postmortem writer deletes any causal claim it cannot cite, the room-restyling app labels every render as a mockup rather than a promise, and the document scanner falls back to on-device recognition instead of failing when the network is gone. That discipline is the service.
What you get
Verification after generation
A prompt is a request, not a control. The guardrails that work run after the model speaks: outputs checked, uncited claims deleted, formats validated before anything reaches a user.
Honest degradation
AI features fail: quotas, outages, dead networks. We design the fallback first. Scanly's mobile app drops to on-device text recognition when the connection is gone, instead of failing the scan.
Cost and latency engineered
Model choice per task, measured against your real data rather than leaderboard folklore. The cheap model where it wins, the strong model where it earns its price.
Privacy decided on purpose
On-device where the product allows it, server-side where it must be, and the boundary stated plainly to users rather than buried.
Proof you can open
Not case studies you have to take on trust. These are live, and you can use them before you ever talk to us.
How we deliver it
02
Guardrail after the model
Verification, citation checks and schema validation on the output path, in code, where a misbehaving model cannot talk its way past them.
03
Evaluate on your data
Bake-offs against real samples with measured error rates, the way we benchmarked five speech engines at telephone quality before choosing one.
04
Ship observable
Every AI call logged with its inputs, outputs and cost, so "why did it say that?" is a query, not a mystery.
Tools we use
ClaudeGPTGeminiPythonFastAPIPostgreSQLEvaluation harnesses
Frequently asked
How do you deal with hallucinations?
By assuming they will happen. Asking a model nicely not to speculate does not work; it speculates politely instead. Our shipped pattern runs verification after generation: any claim that cannot be traced to the source record is deleted or flagged before a user sees it, and tests prove the verifier catches planted faults.
Which models do you build on?
Claude, GPT and Gemini today, chosen per task by measured results on your data. The architecture keeps the model behind an interface, so when a better or cheaper model appears, swapping it is a config change, not a rewrite.
Will our data be used to train models?
The products we build process your content to produce your result. We architect so content is not fed into model training, and where a feature can run on-device instead of a server, we say so and build it that way.
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